The construction of a model is generally divided into two phases, the first consisting of collecting a certain number of data interpreting the functioning of the process and the second consisting of analyzing this data to extract the information necessary for the definition of the model. The presence of a large number of adjustments renders these operations delicate. Moreover, it is impossible in the majority of real cases to perfectly characterize the functioning of a complex process. It is also quite impossible to test all the possible configurations of the adjustment parameters of such processes. Numerous works in the scientific literature concern the determination of a small number of configurations (of experiments) that can allow the total functioning of the process to be characterized at least approximately. The idea is that even an approximate characterization already permits the resolution of a certain number of practical problems.
Intuitively, an effective method consists in repeating several times the cycle of collection and analysis of data. In fact, the analysis of data becomes finer with more experiments conducted under diverse conditions, whereas the realization of these experiments brings all the more information the more precise the analysis and the constructed model are. To illustrate this fact, one example is an industrial process whose productivity is to be optimized by acting on a certain number of adjustments. The expert has a certain knowledge of this process right at the start that permits that expert to determine the most potentially interesting area for each of the adjustments. It is then necessary to perform experiments to optimize this adjustment. The productivity obtained can be measured for each possible configuration of these adjustments. When the possible configurations are too great in number and it is too expensive to test them all, the attempt can be made to determine the influence of each adjustment individually. However, this does not permit the perception of the coupling phenomena that can exist between the adjustments. It is therefore preferable to make all the adjustments vary at the same time but in a coordinated manner. Once the corresponding experiments have been carried out, the expert can refine comprehension of the process that is therefore determined by new experiments to be carried out to test the hypotheses that was formulated. It would therefore be advantageous to provide a system that aids the expert in this step. It is important that the system in question can interact with the expert in a natural and comprehensible manner.
U.S. Pat. No. 6,625,500 discloses a method consisting of generating and automatically realizing experiments in an iterative manner to optimize an industrial process. This method imposes the choice of a number of levels.
US 2002/0128805 discloses a method consisting of the development of a prediction model by automatically generating experiments and comparing their result with the initially calculated prediction.
Those two patents concern only linear or quadratic models with a quantitative output and therefore do not apply to the instance of models with a base of rules and in particular with a qualitative output. Moreover, those two patents aim to automate the entire process and therefore do not permit the integration of the expert knowledge at each cycle of use.